Accord.NET is a framework for scientific computing in .NET. The framework is comprised of multiple librares encompassing a wide range of scientific computing applications, such as statistical data processing, machine learning, pattern recognition, including but not limited to, computer vision and computer audition. The framework offers a large number of probability distributions, hypothesis tests, kernel functions and support for most popular performance measurements techniques.

Accord.Math - Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for contrained and uncontrained problems, special functions and other tools for scientific applications;

Other additions include new statistical hypothesis tests such as Anderson-Daring and Shapiro-Wilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others.

For a complete list of changes, please see the full release notes at the release details page at:

Other additions include new statistical hypothesis tests such as Anderson-Daring and Shapiro-Wilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others.

For a complete list of changes, please see the full release notes at the release details page at:

This release aimed to provide improvements to the documentation. Most of the Univariate Distributions now include proper examples for all main functions and measures in their summary page. Also, a wide set of imaging methods, such as Haralick's set of textural features, the Local Binary Pattern, Gabor, Kirsch, and Variance filters have been added. Also includes the Denavit-Hartenberg model for kinematic chains and many updates, optimizations, corrections and bug-fixes in all major namespaces.

For a complete list of changes, please see the full release notes at the release details page at:

This release brings Cox's proportional hazards models and the partial Newton-Raphson learning algorithm. It also provides a reorganization of the (Hidden Conditional Random) Fields namespace, together with more bugfixes, improvements and optimizations.

For a complete list of changes, please see the full release notes at the release details page.

This release adds support for RProp learning in HCRFs, optimizations to SVM learning and evaluation, a constrained QP solver based on the dual method of Goldfrab and Idnani, robust estimation of fundamental matrices and several other bugfixes and enhancements.

For a complete list of changes, please see the full release notes at http://accord-net.origo.ethz.ch/download/3982